Oracle Data Mining <Insert Picture Here> Oracle 11g DB 11g Release 2 .
Oracle 11g DB Insert Picture Here Data Warehousing ETL OLAP Oracle Data Mining 11g Release 2 Overview and Demo Statistics Data Mining Charlie Berger Sr. Director Product Management, Data Mining Technologies Oracle Corporation charlie.berger@oracle.com Copyright 2009 Oracle Corporation
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Outline Today’s BI must go beyond simple reporting To succeed, companies must Eliminate data movement Collapse information latency Deliver better BI through analytics ODM makes the Database an “Analytical Database” Enables applications “Powered by Oracle Data Mining” Brief demonstrations 1. Oracle Data Mining 2. OBI EE Dashboards with ODM Results 3. Oracle Sales Prospector with embedded ODM Copyright 2009 Oracle Corporation
Analytics: Strategic and Mission Critical Competing on Analytics, by Tom Davenport “Some companies have built their very businesses on their ability to collect, analyze, and act on data.” “Although numerous organizations are embracing analytics, only a handful have achieved this level of proficiency. But analytics competitors are the leaders in their varied fields—consumer products finance, retail, and travel and entertainment among them.” “Organizations are moving beyond query and reporting” - IDC 2006 Super Crunchers, by Ian Ayers “In the past, one could get by on intuition and experience. Times have changed. Today, the name of the game is data.” —Steven D. Levitt, author of Freakonomics “Data-mining and statistical analysis have suddenly become cool. Dissecting marketing, politics, and even sports, stuff this complex and important shouldn't be this much fun to read.” —Wired Copyright 2009 Oracle Corporation
Competitive Advantage Optimization What’s the best that can happen? Competitive Advantage Predictive Modeling What will happen next? Analytic Forecasting/Extrapolation What if these trends continue? Statistical Analysis Why is this happening? Alerts What actions are needed? Query/drill down Where exactly is the problem? Ad hoc reports How many, how often, where? Standard Reports What happened? Degree of Intelligence Source: Competing on Analytics, by T. Davenport & J. Harris Copyright 2009 Oracle Corporation Access & Reporting
Oracle Data Mining Option Copyright 2009 Oracle Corporation
What is Data Mining? Automatically sifts through data to find hidden patterns, discover new insights, and make predictions Data Mining can provide valuable results: Predict customer behavior (Classification) Predict or estimate a value (Regression) Segment a population (Clustering) Identify factors more associated with a business problem (Attribute Importance) Find profiles of targeted people or items (Decision Trees) Determine important relationships and “market baskets” within the population (Associations) Find fraudulent or “rare events” (Anomaly Detection) Copyright 2009 Oracle Corporation
Oracle Data Mining Example Use Cases Retail · Customer segmentation · Response modeling · Recommend next likely product · Profile high value customers Banking · Credit scoring · Probability of default · Customer profitability · Customer targeting Insurance · Risk factor identification · Claims fraud · Policy bundling · Employee retention Higher Education · Alumni donations · Student acquisition · Student retention · At-risk student identification Healthcare · Patient procedure recommendation · Patient outcome prediction · Fraud detection · Doctor & nurse note analysis Life Sciences · Drug discovery & interaction · Common factors in (un)healthy patients · Cancer cell classification · Drug safety surveillance Telecommunications · Customer churn · Identify cross-sell opportunities · Network intrusion detection Public Sector · Taxation fraud & anomalies · Crime analysis · Pattern recognition in military surveillance Copyright 2009 Oracle Corporation Manufacturing · Root cause analysis of defects · Warranty analysis · Reliability analysis · Yield analysis Automotive · Feature bundling for customer segments · Supplier quality analysis · Problem diagnosis Chemical · New compound discovery · Molecule clustering · Product yield analysis Utilities · Predict power line / equipment failure · Product bundling · Consumer fraud detection
Data Mining Provides Better Information, Valuable Insights and Predictions Cell Phone Churners vs. Loyal Customers Segment #3: Income IF CUST MO 7 AND INCOME 175K, THEN Prediction Cell Phone Churner, Confidence 83%, Support 6/39 Insight & Prediction Segment #1: IF CUST MO 14 AND INCOME 90K, THEN Prediction Cell Phone Churner, Confidence 100%, Support 8/39 Customer Months Source: Inspired from Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management by Michael J. A. Berry, Gordon S. Linoff Copyright 2009 Oracle Corporation
Predicting High LTV Customers Using a Decision Tree Model Simple model: Other ODM models can mine: unstructured data (e.g. Mortgage Amount 500K text comments) 500K 2 or More Homes Age 42 LTV HIGH purchases), etc. Age House Own 1 House transactions data (e.g. 35 35 Years Cust 42 2 2 Salary 80K LTV Very High LTV High LTV Low 80K LTV Low LTV Medium IF (Mortgage Amount 500K AND House Own 2 or more AND Age 42) THEN Probability(Lifetime Customer Value is “VERY HIGH” 77%, Support 15% Copyright 2009 Oracle Corporation
“Essentially, all models are wrong, but some are useful.” - George Box (one of the most influential statisticians of the 20th century and a pioneer in the areas of quality control, time series analysis, design of experiments and Bayesian inference.) Copyright 2009 Oracle Corporation
Oracle Data Mining Overview (Classification) Input Attributes Target Historic Data Cases Name Jones Smith Lee Rogers Income 30,000 55,000 25,000 50,000 Campos 40,500 Horn 37,000 Habers 57,200 Berger 95,600 Age . . . . . . . 30 67 23 44 New Data 52 73 32 34 Model Respond? 1 Yes, 0 No 1 1 0 0 ? ? ? ? Prediction 1 0 0 1 Functional Relationship: Y F(X1, X2, , Xm) .85 .74 .93 .65 Confidence Copyright 2009 Oracle Corporation
Oracle Data Mining Algorithm Summary 11g Problem Algorithm Classification Logistic Regression (GLM) Decision Trees Naïve Bayes Support Vector Machine Classical statistical technique Popular / Rules / transparency Embedded app Wide / narrow data / text Regression Multiple Regression (GLM) Support Vector Machine One Class SVM Classical statistical technique Wide / narrow data / text Lack examples Minimum Description Length (MDL) Attribute reduction Identify useful data Reduce data noise Market basket analysis Link analysis Anomaly Detection Attribute Importance Association Rules Clustering A1 A2 A3 A4 A5 A6 A7 Apriori Feature Extraction Applicability Hierarchical K-Means Product grouping Text mining Hierarchical O-Cluster Gene and protein analysis Text analysis Feature reduction NMF F1 F2 F3 F4 Copyright 2009 Oracle Corporation
Traditional Analytics (SAS) Environment Source Data (Oracle, DB2, SQL Server, TeraData, Ext. Tables, etc.) SAS Work Area SAS Processing (Statistical functions/ Data mining) (SAS Work Area) SAS SAS SAS (SAS Datasets) Process Output SAS environment requires: Data movement Data duplication Loss of security Copyright 2009 Oracle Corporation Target (e.g. Oracle)
Oracle Architecture Source Data (Oracle, DB2, SQL Server, TeraData, Ext. Tables, etc.) Oracle environment: Eliminates data movement Eliminates data duplication Preserves security Copyright 2009 Oracle Corporation
In-Database Data Mining Traditional Analytics Oracle Data Mining Results Data Import Data Mining Model “Scoring” Data Preparation and Transformation Savings Data Mining Model Building Data Prep & Transformation Model “Scoring” Data remains in the Database Embedded data preparation Data Extraction Cutting edge machine learning algorithms inside the SQL kernel of Database Model “Scoring” Embedded Data Prep Model Building Data Preparation Hours, Days or Weeks Source Data Faster time for “Data” to “Insights” Lower TCO—Eliminates Data Movement Data Duplication Maintains Security SAS Work Area SAS Proces sing Proces s Output SAS SAS SAS Target Secs, Mins or Hours SQL—Most powerful language for data preparation and transformation Data remains in the Database Copyright 2009 Oracle Corporation
In-Database Data Mining Advantages Oracle 11g DB Data Warehousing ODM architecture provides greater Performance, scalability, and data security Data remains in the database ETL OLAP Statistics Data Mining Fewer moving parts; shorter information latency Straightforward inclusion within interesting and arbitrarily complex queries “SELECT Customers WHERE Income 100K, AND Probability(Buy Product A) .85;” Real-world scalability—available for mission critical appls Enables pipelining of results without costly materialization Performant and scalable: Fast scoring: 2.5 million records scored in 6 seconds on a single CPU system Real-time scoring: 100 models on a single CPU: 0.085 seconds Copyright 2009 Oracle Corporation
HP Oracle Database Machine & ODM Integrated data warehouse solution Extreme Performance 10-100X faster than conventional DW systems Scalability to Petabytes Enterprise-Ready Complete data warehouse functionality Enterprise-level availability and security Scoring of Oracle Data Mining models Blazingly fast performance For example, find the US customers likely to churn: select cust id from customers where region ‘US’ and prediction probability(churnmod, ‘Y’ using *) 0.8; Copyright 2009 Oracle Corporation
HP Oracle Database Machine & ODM In 11gR2, SQL predicates and Oracle Data Mining models are pushed to storage level for execution For example, find the US customers likely to churn: select cust id from customers where region ‘US’ and prediction probability(churnmod,‘Y’ using *) 0.8; CopyrightConfidential 2009 OracleJune Corporation Company 2009
ODM 11gR2 Scoring: Offloaded to Exadata Data mining scoring executed in Exadata: select cust id from customers where region ‘US’ and prediction probability(churnmod, ‘Y’ using *) 0.8; All scoring functions offloaded to Exadata Benefits Reduces data returned from Exadata to Database server Reduces CPU utilization on Database Server Up to 10x performance gains CopyrightConfidential 2009 OracleJune Corporation Company 2009 Scoring function executed in Exadata
“If I had one hour to save the world, I would spend fifty-five minutes defining the problem and only five minutes finding the solution” - Albert Einstein (see also http://www.wikihow.com/Define-a-Problem) Copyright 2009 Oracle Corporation
Where to Start? “Wrong: Catalog everything you have, and decide what data is important. Right: Work backward from the solution, define the problem explicitly, and map out the data needed to populate the investigation and models.” - James Taylor with Neil Raden, authors, Smart (Enough) Systems Copyright 2009 Oracle Corporation
Oracle Data Mining and Unstructured Data Oracle Data Mining mines unstructured i.e. “text” data Include free text and comments in ODM models Cluster and Classify documents Oracle Text used to preprocess unstructured text Copyright 2009 Oracle Corporation
Example: Simple, Predictive SQL Select customers who are more than 85% likely to be HIGH VALUE customers & display their AGE & MORTGAGE AMOUNT SELECT * from( SELECT A.CUSTOMER ID, A.AGE, MORTGAGE AMOUNT,PREDICTION PROBABILITY (INSUR CUST LT13126 DT, 'VERY HIGH' USING A.*) prob FROM CBERGER.INSUR CUST LTV A) WHERE prob 0.85; Copyright 2009 Oracle Corporation
Fraud Prediction Demo drop table CLAIMS SET; exec dbms data mining.drop model('CLAIMSMODEL'); create table CLAIMS SET (setting name varchar2(30), setting value varchar2(4000)); insert into CLAIMS SET values ('ALGO NAME','ALGO SUPPORT VECTOR MACHINES'); insert into CLAIMS SET values ('PREP AUTO','ON'); commit; begin dbms data mining.create model('CLAIMSMODEL', 'CLASSIFICATION', 'CLAIMS', 'POLICYNUMBER', null, 'CLAIMS SET'); end; / -- Top 5 most suspicious fraud policy holder claims select * from (select POLICYNUMBER, round(prob fraud*100,2) percent fraud, rank() over (order by prob fraud desc) rnk from (select POLICYNUMBER, prediction probability(CLAIMSMODEL, '0' using *) prob fraud from CLAIMS where PASTNUMBEROFCLAIMS in ('2 to 4', 'more than 4'))) where rnk 5 order by percent fraud desc; Copyright 2009 Oracle Corporation POLICYNUMBER PERCENT FRAUD RNK ------------ ------------- ---------- 6532 64.78 1 2749 64.17 2 3440 63.22 3 654 63.1 4 12650 62.36 5
Oracle Data Mining 11g Data Mining Functions (Server) PL/SQL & Java APIs Develop & deploy predictive analytics applications Wide range of DM algorithms (12) Classification & regression Clustering Anomaly detection Attribute importance Feature extraction (NMF) Association rules (Market Basket analysis) Structured & unstructured data (text mining) Oracle Data Miner (GUI) Simplified, guided data mining using wizards Predictive Analytics “1-click data mining” from a spreadsheet Copyright 2009 Oracle Corporation Oracle 11g 11g DB Data Warehousing ETL OLAP Statistics Data Mining
Analytical Database Changes*Everything* It boils down to this: Less data movement faster analytics, and faster analytics better BI throughout the enterprise OLAP Predictive Analytics Statistical Functions ?x Text Mining Data Mining Copyright 2009 Oracle Corporation
Integration with Oracle BI EE Oracle Data Mining results available to Oracle BI EE administrators Oracle BI EE defines results for end user presentation Copyright 2009 Oracle Corporation
Example Better Information for OBI EE Reports and Dashboards ODM’s predictions Predictions & & probabilities are available available in in the Database Database for for reporting Oracle BI EE using Oracle and other BI EE andtools reporting other tools Copyright 2009 Oracle Corporation
Oracle SQL Statistical Functions (Free in Every Oracle Database) Copyright 2009 Oracle Corporation
11g Statistics & SQL Analytics Ranking functions Statistics Descriptive Statistics rank, dense rank, cume dist, percent rank, ntile Window Aggregate functions (moving and cumulative) Avg, sum, min, max, count, variance, stddev, first value, last value LAG/LEAD functions Direct inter-row reference using offsets Reporting Aggregate functions Sum, avg, min, max, variance, stddev, count, ratio to report Statistical Aggregates Correlation, linear regression family, covariance Linear regression Fitting of an ordinary-least-squares regression line to a set of number pairs. Frequently combined with the COVAR POP, COVAR SAMP, and CORR functions DBMS STAT FUNCS: summarizes numerical columns of a table and returns count, min, max, range, mean, stats mode, variance, standard deviation, median, quantile values, /- n sigma values, top/bottom 5 values Correlations Pearson’s correlation coefficients, Spearman's and Kendall's (both nonparametric). Cross Tabs Enhanced with % statistics: chi squared, phi coefficient, Cramer's V, contingency coefficient, Cohen's kappa Hypothesis Testing Student t-test , F-test, Binomial test, Wilcoxon Signed Ranks test, Chi-square, Mann Whitney test, Kolmogorov-Smirnov test, One-way ANOVA Distribution Fitting Kolmogorov-Smirnov Test, Anderson-Darling Test, Chi-Squared Test, Normal, Uniform, Weibull, Exponential Note: Statistics and SQL Analytics are included in Oracle Database Standard Edition Copyright 2009 Oracle Corporation
Descriptive Statistics MEDIAN & MODE SQL Median: takes numeric or datetype values and returns the middle value Mode: returns the most common value A. SELECT STATS MODE(AGE) from LYMPHOMA; B. SELECT MEDIAN(AGE) from LYMPHOMA; C. SELECT TREATMENT PLAN, STATS MODE(LYMPH TYPE) from lymphoma GROUP BY TREATMENT PLAN; D. SELECT LYMPH TYPE, MEDIAN(SIZE REDUCTION) from LYMPHOMA GROUP BY LYMPH TYPE ORDER BY MEDIAN(SIZE REDUCTION) ASC; Copyright 2009 Oracle Corporation
Split Lot A/B Offer testing Offer “A” to one population and “B” to another Over time period “t” calculate median purchase amounts of customers receiving offer A & B Perform t-test to compare If statistically significantly better results achieved from one offer over another, offer everyone higher performing offer Copyright 2009 Oracle Corporation
Independent Samples T-Test (Pooled Variances) Query compares the mean of AMOUNT SOLD between MEN and WOMEN within CUST INCOME LEVEL ranges SELECT substr(cust income level,1,22) income level, avg(decode(cust gender,'M',amount sold,null)) sold to men, avg(decode(cust gender,'F',amount sold,null)) sold to women, stats t test indep(cust gender, amount sold, 'STATISTIC','F') t observed, stats t test indep(cust gender, amount sold) two sided p value FROM sh.customers c, sh.sales s WHERE c.cust id s.cust id GROUP BY rollup(cust income level) ORDER BY 1; SQL Worksheet Copyright 2009 Oracle Corporation
?x Correlation Functions The CORR S and CORR K select CORR S(AGE, WEIGHT) functions support nonparametric or coefficient, rank correlation (finding correlations CORR S(AGE, WEIGHT, between expressions that are ordinal 'TWO SIDED SIG') scaled). p value, Correlation coefficients take on a substr(TREATMENT PLAN, 1,15) value ranging from –1 to 1, where: 1 indicates a perfect relationship –1 indicates a perfect inverse relationship 0 indicates no relationship as TREATMENT PLAN from CBERGER.LYMPHOMA GROUP BY TREATMENT PLAN; The following query determines whether there is a correlation between the AGE and WEIGHT of people, using Spearman's correlation: Copyright 2009 Oracle Corporation
Analytics vs. SAS 1. In-Database Analytics Engine Basic Statistics (Free) Data Mining Text Mining 1. External Analytical Engine Basic Statistics Data Mining Text Mining (separate: SAS EM for Text) Advanced Statistics 2. Costs (ODM: 23K cpu) Simplified environment Single server Security 2. Costs (SAS EM: 150K/5 users) Duplicates data Annual Renewal Fee (AUF) 3. IT Platform SQL (standard) Java (standard) 3. IT Platform SAS Code (proprietary) ( 45% each year) Oracle 11g DB Data Warehousing SAS ETL OLAP Statistics Data Mining Copyright 2009 Oracle Corporation
Analytics vs. SAS 1. In-Database Analytics Engine Basic Statistics (Free) Data Mining Text Mining 1. External Analytical Engine Basic Statistics Data Mining Text Mining (separate: SAS EM for Text) Advanced Statistics 2. Costs (ODM: 23K cpu) Simplified environment Single server Security 2. Costs (SAS EM: 150K/5 users) Duplicates data Annual Renewal Fee (AUF) 3. IT Platform SQL (standard) Java (standard) 3. IT Platform SAS Code (proprietary) Oracle 11g DB Data Warehousing ( 45% each year) Oracle 11g DB Data Warehousing ETL ETL OLAP Statistics OLAP Statistics Data Mining Data Mining Copyright 2009 Oracle Corporation SAS
SAS In-Database Processing 3-Year Road Map “The goal of the SAS In-Database initiative is to achieve deeper technical integration with database providers. , the SAS engine often must load and extract data over a network to and from the DBMS. This presents a series of challenges: Network bottlenecks between SAS and the DBMS constrain access to large volumes of data the results of the SAS processing must be transferred back to the DBMS for final storage, which further increases the cost. Source: SAS In-Database Processing White Paper—October 2007 Copyright 2009 Oracle Corporation
IDC Worldwide Business Analytics Software Oracle frastructure/bi dw/208699e.pdf Copyright 2009 Oracle Corporation
Brief Demonstrations 1. Oracle Data Mining 2. Oracle Business Intelligence EE 3. CRM Sales Prospector Copyright 2009 Oracle Corporation
Oracle Data Mining OBI EE Copyright 2009 Oracle Corporation
Quick Demo: Oracle Data Mining Scenario: Insurance Company Business problem(s): 1. Better understand the business by looking at graphs of the data 2. Identify the factors (attributes) most associated with Customer who BUY INSURANCE 3. Target Best Customers a. Build a predictive model to understand who will be a VERY HIGH VALUE Customer . And WHY (IF THEN. Rules that can describe them) b. Predict who is likely to be a VERY HIGH VALUE Customer in the future c. View results in an OBI EE Dashboard Including other business problems e.g. Fraud, Cross-Sell, etc. (Entire process can be automated w/ PL/SQL and/or Java APIs) Copyright 2009 Oracle Corporation
Oracle Data Mining OBI EE Understand the Data Oracle Data Mining helps to visualize the data Copyright 2009 Oracle Corporation
Oracle Data Mining OBI EE Target the Right Customers Oracle Data Miner guides the analyst through the data mining process Copyright 2009 Oracle Corporation
Oracle Data Mining OBI EE Targeting High Value Customers Oracle Data Mining builds a model that differentiates HI VALUE CUSTOMERS from others Copyright 2009 Oracle Corporation
Oracle Data Mining OBI EE Targeting High Value Customers Oracle Data Mining creates a prioritized list of customer who are likely to be high value Copyright 2009 Oracle Corporation
Integration with Oracle BI EE Oracle Data Mining provides more information and better insight Copyright 2009 Oracle Corporation
Oracle Data Mining Know More, Do More, Spend Less Business Decision Makers Make Better Decisions Extract More Value from Your Data Lower Your Total Cost of Ownership Data Analysts Integrators and IT Get Results Faster Get More Results Easy to Use Copyright 2009 Oracle Corporation Create More Value for Your Organization Make Your Work Easier Transform IT from a Cost to a Profit Center
Oracle Data Mining (SQL & Java) APIs Copyright 2009 Oracle Corporation
HCM Prediction Demo drop table HCM SET; exec dbms data mining.drop model('HCMMODEL'); create table HCM SET (setting name varchar2(30), setting value varchar2(4000)); insert into HCM SET values ('ALGO NAME','ALGO SUPPORT VECTOR MACHINES'); insert into HCM SET values ('PREP AUTO','ON'); commit; ACTUAL PERCENT CORR INCORR TOTAL ------------ ---------- ---------- ---------- ---------- NO 84.04 3133 595 3728 YES 80.61 8159 1963 10122 81.53 11292 2558 13850 Elapsed: 00:00:01.51 SQL begin dbms data mining.create model('HCMMODEL', 'CLASSIFICATION', 'EMPL DATA', 'EMPL ID', 'CURR EMPL', 'HCM SET'); end; / -- accuracy (per-class and overall) col actual format a6 select actual, round(corr*100/total,2) percent, corr, total-corr incorr, total from (select actual, sum(decode(actual,predicted,1,0)) corr, count(*) total from (select CURR EMPL actual, prediction(HCMMODEL using *) predicted from EMPL DATA JUNE07) group by rollup(actual)); -- top 5 very high value, current employees most likely to leave select * from (select empl id, round(prob leave*100,2) percent leave, rank() over (order by prob leave desc) rnk from (select empl id, prediction probability(HCMMODEL, 'NO' using *) prob leave from EMPL DATA JUNE07 where CURR EMPL 'YES' and LTV BIN 'VERY HIGH')) where rnk 5 order by percent leave desc; Copyright 2009 Oracle Corporation EMPL ID PERCENT LEAVE RNK ---------- ------------- ---------- 772858 96.84 1 775441 95.65 2 777992 92.1 3 773473 91.51 4 771813 90.21 5 Elapsed: 00:00:00.29 SQL
Predictive Analytics Use Case The cast: Peter: a data mining analyst Sally: a marketing manager Peter builds a decision tree classification model, tree model Peter grants the ability to view/score the tree model to Sally GRANT SELECT MODEL ON tree model TO Sally; Sally inspects the model, likes it, and wants it deployed Sally scores the customer database using the new model and his understanding of the cost of contacting a customer and sends the new contact list to the head of the sales department CREATE TABLE AS SELECT cust name, cust phone FROM customers WHERE prediction(Peter.tree model cost matrix (0,5,1,0) using *) ‘responder’; Copyright 2009 Oracle Corporation
Real-time Prediction with records as (select On-the-fly, single record 78000 SALARY, 250000 MORTGAGE AMOUNT, apply with new data (e.g. 6 TIME AS CUSTOMER, from call center) 12 MONTHLY CHECKS WRITTEN, 55 AGE, 423 BANK FUNDS, 'Married' MARITAL STATUS, 'Nurse' PROFESSION, 'M' SEX, 4000 CREDIT CARD LIMITS, 2 N OF DEPENDENTS, 1 HOUSE OWNERSHIP from dual) select s.prediction prediction, s.probability probability from ( select PREDICTION SET(INSUR CUST LT68054 DT, 1 USING *) pset from records) t, TABLE(t.pset) s; Copyright 2009 Oracle Corporation
Prediction Multiple Models/Optimization ¾ with records as (select 178255 ANNUAL INCOME, 30 AGE, 'Bach.' EDUCATION, 'Married' MARITAL STATUS, 'Male' SEX, 70 HOURS PER WEEK, 98 PAYROLL DEDUCTION from dual) select t.* from ( select 'CAR MODEL' MODEL, s1.prediction prediction, s1.probability probability, s1.probability*25000 as expected revenue from ( select PREDICTION SET(NBMODEL JDM, 1 USING *) pset from records ) t1, TABLE(t1.pset) s1 UNION select 'MOTOCYCLE MODEL' MODEL, s2.prediction prediction, s2.probability probability, s1.probability*2000 as expected revenue from ( select PREDICTION SET(ABNMODEL JDM, 1 USING *) pset from records ) t2, TABLE(t2.pset) s2 UNION select 'TRICYCLE MODEL' MODEL, s3.prediction prediction, s3.probability probability, s1.probability*50 as expected revenue from ( select PREDICTION SET(TREEMODEL JDM, 1 USING *) pset from records ) t3, TABLE(t3.pset) s3 UNION select 'BICYCLE MODEL' MODEL, s4.prediction prediction, s4.probability probability, s1.probability*200 as expected revenue from ( select PREDICTION SET(SVMCMODEL JDM, 1 USING *) pset from records ) t4, TABLE(t4.pset) s4 ) t On-the-fly, multiple models; then sort by expected revenues order by t.expected revenue desc; Copyright 2009 Oracle Corporation
Oracle Sales Prospector Copyright 2009 Oracle Corporation
Larry Ellison Oracle Open World Keynote November 2007 Announces Fusion Edge CRM On-Demand Hosted Application with integrated data mining to mine customer database Oracle Data Mining Copyright 2009 Oracle Corporation
How Can I Sell More? Which prospects most resemble those customers? Which types of customers are buying which products? Which references can I use to help me close my deals? Customers References Products Sales Rep Copyright 2009 Oracle Corporation
Oracle Data Mining the Science of Selling Oracle Sales Prospector ODM Predictions exposed via Social CRM Dashboards Oracle Database 11G Social CRM schema ships with Oracle Database EE 11g Data Mining Option Copyright 2009 Oracle Corporation
Oracle Data Mining predicts likelihood of purchases Oracle Data Mining recommends products customer is likely to buy Copyright 2009 Oracle Corporation Oracle Data Mining suggests likely references
Oracle Retail Data Model Copyright 2009 Oracle Corporation
Oracle Retail Data Model Out-of-the box, Oracle Data Mining generates profiles of customers Copyright 2009 Oracle Corporation Oracle Data Mining automatically mines data for analysis reports
Summary Copyright 2009 Oracle Corporation
Oracle Data Mining Summary Powers Next-Generation Predictive Applications Rapidly Build Applications that Automatically Mine Data Code Once, Run Anywhere Parallel and Distributed Processing Industry Standard SQL and Java APIs Industry Leader in In-Database Data Mining Option to the Industry Leading RDBMS—Oracle Database Classification, Regression, Attribute Importance Clustering, Market Basket Analysis, Anomaly Detection, Feature Extraction Cutting Edge Algorithms: SVM, One-Class SVM, NMF, Scalable GLM Copyright 2009 Oracle Corporation
Oracle Data Mining Summary More Information from More Data Easy to use Oracle Data Miner Graphical User Interface Wide Range of In-Database Data Mining Algorithms and Statistics Mine Text, Transactional, and Star Schema Data Mine XML, Semantic RDF, Spatial, and OLAP Data Eliminate Barriers Between Analysts and IT Quickly Disseminate Analytical Results and Models Throughout the Organization Include Real-Time Predictive Models and New Insights in SQL queries Eliminate Data Movement, Maximize Security Copyright 2009 Oracle Corporation
Getting Started Copyright 2009 Oracle Corporation
Data Mining Projects “The vast majority of BI professionals are excited about the prospects of data mining, but are fully mystified about where to begin or even how to prepare” “Of those who did initiate a modeling initiative, 51% of data mining projects either never left the ground, did not realize value or the ultimate results were not measurable” “In most cases, those who attempted an implementation ended up building excellent predictive models that answer the wrong questions” “For any organization with annual revenues more than 50 million, employing data mining technology is not a matter of whether, but when” http://www.the-modeling-agency.com Copyright 2009 Oracle Corporation
Getting Started with Oracle Data Mining You can download a free evaluation copy of Oracle Data Mining and try it out on your own computer. See the Oracle Data Mini
In-Database Data Mining Traditional Analytics Hours, Days or Weeks Data Extraction Data Prep & Transformation Data Mining Model Building Data Mining Model "Scoring" Data Preparation and Transformation Data Import Source Data SAS Work Area SAS Proces sing Proces s Output Target Results Faster time for "Data" to "Insights .
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Oracle Advanced Analytics (Oracle Data Mining and Oracle R Enterprise) Data Warehousing Oracle OLAP, Oracle Spatial, OBIEE Expert presenters at major Oracle conferences www.vlamis.com (blog, papers, newsletters, services) Co-author of book “Oracle Essbase & Oracle OLAP” Beta tester for OBIEE 11g, Oracle 12c (in-memory)
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Preface to the First Edition xv 1 DATA-MINING CONCEPTS 1 1.1 Introduction 1 1.2 Data-Mining Roots 4 1.3 Data-Mining Process 6 1.4 Large Data Sets 9 1.5 Data Warehouses for Data Mining 14 1.6 Business Aspects of Data Mining: Why a Data-Mining Project Fails 17 1.7 Organization of This Book 21 1.8 Review Questions and Problems 23
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